Evolution of tuberculosis diagnostics: From molecular strategies to nanodiagnostics

Enhanced detection of rifampicin and isoniazid resistance in mycobacterium tuberculosis using AuNP-qPCR: a rapid and accurate method

OBJECTIVES

To evaluate the resistance of Mycobacterium tuberculosis to Rifampicin (RIF) and Isoniazid (INH) using enhanced qPCR methodologies.

METHODS

This study compared the detection of drug-resistant mutations in the rpoB and katG genes using AuNP-qPCR and No-AuNP-qPCR. Calibration curves were constructed to correlate the amount of template with the Ct values for resistant strains.

RESULTS

The AuNP-qPCR method demonstrated high efficacy in detecting RIF resistance with an area under the curve (AUC) of 0.951, sensitivity of 97.92%, specificity of 87.5%, and overall accuracy of 95.31%. Similarly, INH resistance detection by AuNP-qPCR showed an AUC of 0.981, sensitivity of 98.08%, specificity of 94.44%, and accuracy of 97.14%. Comparatively, No-AuNP-qPCR yielded lower performance metrics for RIF resistance (AUC: 0.867, sensitivity: 91.67%, specificity: 75%, accuracy: 87.5%) and INH resistance (AUC: 0.882, sensitivity: 88.46%, specificity: 83.33%, accuracy: 87.14%).

CONCLUSIONS

AuNP-qPCR exhibits over traditional qPCR methods, making it a promising tool for rapid and precise detection of drug resistance in Mycobacterium tuberculosis. This method's robust performance underscores its potential to improve diagnostic protocols and contribute to more effective management of tuberculosis treatment.

Self-Trained Convolutional Neural Network (CNN) for Tuberculosis Diagnosis in Medical Imaging

Background Tuberculosis (TB) is a serious infectious disease that primarily affects the lungs. Despite advancements in the medical industry, TB remains a significant global health challenge. Early and accurate detection of TB is crucial for effective treatment and reducing transmission. This article presents a deep learning approach using convolutional neural networks (CNNs) to improve TB detection in chest X-ray images. Methods For the dataset, we collected 7000 images from Kaggle.com, of which 3500 exhibit tuberculosis evidence and the remaining 3500 are normal. Preprocessing techniques such as wavelet transformation, contrast-limited adaptive histogram equalisation (CLAHE), and gamma correction were applied to enhance the image quality. Random flipping, random rotation, random resizing, and random rescaling were among the techniques employed to increase dataset variability and model robustness. Convolutional, max-pooling, flatten, and dense layers comprised the CNN model architecture. For binary classification, sigmoid activation was utilised in the output layer and rectified linear unit (ReLU) activation in the input and hidden layers. Results The CNN model achieved an accuracy of ~96.57% in detecting TB from chest X-ray images, demonstrating the effectiveness of deep learning, particularly CNNs, in this application. Self-trained CNNs have optimised the results as compared to the transfer learning of various pre-trained models. Conclusion This study shows how well deep learning-in particular, CNNs-performs in the identification of tuberculosis. Subsequent efforts have to give precedence to optimising the model by obtaining more extensive datasets from the local hospitals and localities, which are vulnerable to TB, and stress the possibility of augmenting diagnostic knowledge in medical imaging via machine learning methodologies.

Development and evaluation of a single-tube nested PCR with colorimetric assay for Mycobacterium tuberculosis detection

Molecular techniques have revolutionized tuberculosis (TB) diagnosis by offering a faster and more sensitive approach, detecting Mycobacterium tuberculosis (Mtb) DNA directly from samples. Single-tube nested PCR (STNPCR) combines two PCR reactions with separate oligonucleotide sets in a single tube. Moreover, colorimetric methods in PCR products have been studied for pathogen detection. Thus, this study aimed to establish a novel system based on colorimetric STNPCR for Mtb detection using microtiter plates with IS6110-amplified fragments. The results showed a general colorimetric STNPCR detection limit of 1 pg/μl. Its general sensitivity and specificity were 76.62 and 60.53%, respectively, with kappa index agreement of 0.166.

Application of CRISPR-cas-based technology for the identification of tuberculosis, drug discovery and vaccine development

Tuberculosis (TB), which caused by Mycobacterium tuberculosis, is the leading cause of death from a single infectious agent and continues to be a major public health burden for the global community. Despite being the only globally licenced prophylactic vaccine, Bacillus Calmette-Guérin (BCG) has multiple deficiencies, and effective diagnostic and therapeutic options are limited. Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas (CRISPR-associated proteins) is an adaptive immune system that is found in bacteria and has great potential for the development of novel antituberculosis drugs and vaccines. In addition, CRISPR-Cas is currently recognized as a prospective tool for the development of therapies for TB infection with potential diagnostic and therapeutic value, and CRISPR-Cas may become a viable tool for eliminating TB in the future. Herein, we systematically summarize the current applications of CRISPR-Cas-based technology for TB detection and its potential roles in drug discovery and vaccine development.

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

The spread of tuberculosis (TB), especially multidrug-resistant TB and extensively drug-resistant TB, has strongly motivated the research and development of new anti-TB drugs. New strategies to facilitate drug combinations, including pharmacokinetics-guided dose optimization and toxicology studies of first- and second-line anti-TB drugs have also been introduced and recommended. Liquid chromatography-mass spectrometry (LC-MS) has arguably become the gold standard in the analysis of both endo- and exo-genous compounds. This technique has been applied successfully not only for therapeutic drug monitoring (TDM) but also for pharmacometabolomics analysis. TDM improves the effectiveness of treatment, reduces adverse drug reactions, and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window. Based on TDM, the dose would be optimized individually to achieve favorable outcomes. Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs, aiding in the discovery of potential biomarkers for TB diagnostics, treatment monitoring, and outcome evaluation. This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades. Besides, we discussed the advantages and disadvantages of this technique in practical use. The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted. Lastly, we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies (pharmacometrics, drug and vaccine developments, machine learning/artificial intelligence, among others) to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

COVID-19 and Tuberculosis: Unveiling the Dual Threat and Shared Solutions Perspective

The year 2020 will likely be remembered as the year dominated by COVID-19, or coronavirus disease. The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for this pandemic, can be traced back to late 2019 in China. The COVID-19 pandemic has significantly impacted the tuberculosis (TB) care system, reducing TB testing and reporting. This can be attributed to the disruption of TB services and restrictions on patient movement, consequently increasing TB-related deaths. This perspective review aims to highlight the intersection between COVID-19 and TB, highlighting their dual threat and identifying shared solutions to address these two infectious diseases effectively. There are several shared commonalities between COVID-19 and tuberculosis, particularly the transmission of their causative agents, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Mycobacterium tuberculosis. Both pathogens are transmitted via respiratory tract secretions. TB and COVID-19 are diseases that can be transmitted through droplets and airborne particles, and their primary target is typically the lungs. Regarding COVID-19 diagnostics, several methods are available for rapid and accurate detection. These include RT-PCR, which can provide results within two hours, and rapid antigen test kits that offer results in just a few minutes. The availability of point-of-care self-testing further enhances convenience. On the other hand, various approaches are employed for TB diagnostics to swiftly identify active TB. These include sputum microscopy, sputum for reverse transcription polymerase chain reaction (RT-PCR), and chest X-rays. These methods enable the rapid detection of active TB on the same day, while culture-based testing may take significantly longer, ranging from 2 to 8 weeks. The utilization of diverse diagnostic tools helps ensure the timely identification and management of COVID-19 and TB cases. The quality of life of patients affected by COVID-19 and tuberculosis (TB) can be significantly impacted due to the nature of these diseases and their associated challenges. In conclusion, it is crucial to emphasize the urgent need to address the dual threat of COVID-19 and TB. Both diseases have devastated global health, and their convergence poses an even greater challenge. Collaborative efforts, research investments, and policy reforms are essential to tackle this dual threat effectively.